---

name: nvidia-rag-blueprint description: 'NVIDIA RAG Blueprint — deploy, configure, troubleshoot, and manage. Handles any RAG action: deploy, install, start, enable, disable, toggle, change, configure, troubleshoot, debug, fix, shutdown, stop, or tear down any RAG feature or service (VLM, guardrails, query rewriting, models, search, ingestion, observability, summarization, and more).' license: Apache-2.0 allowed-tools: Bash(echo *), Bash(nvidia-smi *), Bash(curl *), Bash(docker ps *), Bash(docker exec *), Bash(docker info *), Bash(docker --version *), Bash(docker compose version *), Bash(docker logs *), Bash(docker system *), Bash(kubectl get *), Bash(kubectl describe *), Bash(kubectl version *), Bash(kubectl logs *), Bash(helm version *), Bash(helm list *), Bash(git rev-parse *), Bash(git describe *), Bash(git status *), Bash(python3 --version *), Bash(pip3 show *), Bash(df *), Bash(du ), Bash(cat /proc/), Bash(cat /etc/os-release *), Bash(ss *), Bash(netstat *), Bash(ls *), Bash(grep *), Bash(lsof *), Bash(ps aux *), Read, Grep, Glob metadata: author: nvidia-rag-team version: '1.0' argument-hint: deploy RAG | enable feature | disable feature | configure | troubleshoot | shutdown

NVIDIA RAG Blueprint

Autonomy Principles

• Auto-detect everything: GPU, VRAM, drivers, Docker, CUDA, disk, OS, ports, existing services, NGC key, repo state.
• If it can be checked with a command, check it — don't ask the user.
• Ask only when user action is required: providing an API key, confirming data deletion, or choosing between equally valid options.
• Once analysis is done, route to the correct workflow and execute.

Intent Detection

Determine what the user wants and route immediately:

User Intent	Action
Deploy, install, set up, start RAG	Read and follow references/deploy.md
Configure, enable, change, toggle a feature	Use the Configure section below
Troubleshoot, debug, fix, error, unhealthy	Read and follow references/troubleshoot.md
Stop, shutdown, tear down, clean up	Read and follow references/shutdown.md

If the intent is ambiguous, infer from context (e.g., "RAG isn't working" → troubleshoot; "get RAG running" → deploy). Only ask if genuinely unclear.

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Configure

Requires a running RAG deployment. If services are not running, deploy first via references/deploy.md.

Match the user's request to a reference file, then read and follow it:

Feature Keywords	Reference
VLM, VLM embeddings, image captioning	references/configure/vlm.md
NeMo Guardrails	references/configure/guardrails.md
Query rewriting, decomposition, multi-turn	references/configure/query-and-conversation.md
Ingestion (text-only, audio, Nemotron Parse, OCR, batch CLI, NV-Ingest, volume mount, performance)	references/configure/ingestion.md
Search, retrieval, hybrid search, multi-collection, metadata, filters, reranker, topK, accuracy/performance	references/configure/search-and-retrieval.md
LLM/embedding/ranking model changes, vector DB, Milvus/Elasticsearch auth, service keys, model profiles, ports/GPU	references/configure/models-and-infrastructure.md
Reasoning, self-reflection, prompts, generation params (tokens, temperature, citations), per-request LLM params	references/configure/reasoning-and-generation.md
Summarization	references/configure/summarization.md
Observability (tracing, Zipkin, Grafana, Prometheus)	references/configure/observability.md
Multimodal query (image + text)	references/configure/multimodal-query.md
Data catalog (collection/document metadata)	references/configure/data-catalog.md
User interface (UI settings)	references/configure/user-interface.md
API reference (endpoints, schemas)	references/configure/api-reference.md
Evaluation (RAGAS metrics)	references/configure/evaluation.md
MCP server & client, agent toolkit	references/configure/mcp.md
Migration (version upgrades)	references/configure/migration.md
Notebooks (setup and catalog)	references/configure/notebooks.md

Configure Flow

1. Match the user's request to a reference file from the table above.

2. Detect what's running:

   echo "=== NIM ===" && docker ps --format '{{.Names}}' 2>/dev/null | grep -iE '(nim-llm|nemoretriever-embedding|nemoretriever-ranking|nemo-vlm|nemotron-vlm)' || echo "NO_LOCAL_NIMS"; echo "=== RAG ===" && docker ps --format '{{.Names}}' 2>/dev/null | grep -iE '(rag-server|ingestor-server|milvus)' || echo "NO_DOCKER_RAG"; echo "=== K8S ===" && kubectl get pods -n rag 2>/dev/null | head -5 || echo "NO_K8S"; echo "=== LIBRARY ===" && ps aux 2>/dev/null | grep -E '(nvidia_rag|uvicorn.*rag)' | grep -v grep || echo "NO_LIBRARY"
   

3. Use this table to determine platform, deployment type, and where config lives:

   Local NIMs running?	RAG services running?	Deployment Type	Config Location
   Yes (Docker)	Any	Self-hosted	deploy/compose/.env
   No	Yes (Docker)	NVIDIA-hosted	deploy/compose/nvdev.env
   Yes (K8s pods)	Any	Self-hosted	values.yaml (NIM sections)
   No	Yes (K8s pods)	NVIDIA-hosted	values.yaml (envVars)
   —	Library processes	Library mode	notebooks/config.yaml
   No	No	Not running	Deploy first via references/deploy.md

   Tell the user what you detected and ask to confirm. Example: "I see local NIM containers running (nim-llm-ms, nemoretriever-embedding-ms) — this is a self-hosted deployment. Config file is deploy/compose/.env. Correct?"

4. Check current feature state before changing anything — read the config location from step 3, then cross-check the live service:

   • Docker: docker exec rag-server env 2>/dev/null | grep -E "<VAR_NAME>"
   • Helm: kubectl get pod -n rag -l app=rag-server -o jsonpath='{.items[0].spec.containers[0].env}' 2>/dev/null

   If the config file and live service disagree, tell the user the service has stale config and will need a restart.

5. If the feature needs extra GPUs, check availability against hardware restrictions (see below):

   nvidia-smi --query-gpu=index,name,memory.total,memory.used --format=csv,noheader 2>/dev/null || echo "NO_GPU"
   

6. Read the reference file and apply changes:

   • Docker: edit the env file (uncomment to enable, re-comment to disable — the env file is the source of truth). Then restart the affected service:

     source <env-file> && docker compose -f deploy/compose/<compose-file> up -d
     

     Service	Compose File
     rag-server	docker-compose-rag-server.yaml
     ingestor-server	docker-compose-ingestor-server.yaml
     milvus, etcd, minio	vectordb.yaml
     NIM containers (LLM, embedding, ranking, VLM, OCR)	nims.yaml
     guardrails	docker-compose-nemo-guardrails.yaml
     observability (Grafana, Prometheus, Zipkin)	observability.yaml

   • Helm: edit values.yaml, then upgrade: helm upgrade rag <chart> -n rag -f values.yaml
   • Library: edit notebooks/config.yaml, then restart the Python process

7. Verify:

   • Docker: docker ps --format "table {{.Names}}\t{{.Status}}" | head -20; curl -s http://localhost:8081/v1/health?check_dependencies=true 2>/dev/null | head -1
   • Helm: kubectl get pods -n rag; kubectl rollout status deployment/rag-server -n rag --timeout=120s
   • Library: curl -s http://localhost:8081/v1/health 2>/dev/null | head -1

8. If restart fails, read references/troubleshoot.md. If multiple features requested, repeat from step 1 for each.

When User Says "Configure" Without Specifics

Run steps 2–3 above, then read the identified config file to list what's currently enabled:

grep -E "^(export )?(ENABLE_|APP_)" <config-file> 2>/dev/null | sort

Summarize what's running and enabled, then ask which feature to change.

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Hardware Restrictions

Read docs/support-matrix.md for current GPU requirements per deployment mode. Read docs/service-port-gpu-reference.md for port mappings and GPU assignments.

GPU	Feature Restrictions
B200	No VLM, No Guardrails, No Nemotron Parse. May need multi-GPU LLM (LLM_MS_GPU_ID).
RTX PRO 6000	No Nemotron Parse. No Audio on Helm.